As data-vizzers, we need to focus our user interface and user experience in a way that the greater population already understands.

With that in mind, I decided to develop this application using a collapsible-menu system that we see in many mobile apps.

Let’s take a look at wide receivers, for example: Antonio Brown was the top receiver in most scoring formats, and he will likely be a top three draft pick in most leagues.

I wouldn’t fault anyone for taking Brown with the top pick, but if you end up near the end of your draft order, consider Brandon Marshall.

After only a few minutes of searching, I found an NFL data API built for Python named nflgame.

Quickly scanning examples and documentation led me to write this code: import nflgame import csv import os #Define a list for every week in the NFL regular season weeks = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17] #For every week in the weeks list, grab the player game logs and create a csv file for each week for week in weeks: nflgame.combine(nflgame.games(2015, week=week)).csv(str(week) '.csv') #The previous function created an empty row between each row of data.

In order for the draft model to scale, the application needed to be simple to use and have a minimal amount of views that covered a wide range of analyses.

To design for simplicity means to design for commonality.

It’s also the reason why we don’t need to see trend charts in this dashboard.